BACKGROUND AND OBJECTIVES:

The coronavirus disease 2019 (COVID-19) pandemic has impacted hospitals, potentially affecting quality and safety. Our objective was to compare pediatric hospitalization safety events during the pandemic versus previous years.

METHODS:

In this retrospective cohort study of hospitalizations in the Pediatric Health Information System, we compared Pediatric Quality Indicator (PDI) rates from March 15 to May 31, 2017–2019 (pre-COVID-19), with those from March 15 to May 31, 2020 (during COVID-19). Generalized linear mixed-effects models with adjustment for patient characteristics (eg, diagnosis, clinical severity) were used.

RESULTS:

There were 399 113 discharges pre-COVID-19 and 88 140 during COVID-19. Unadjusted PDI rates were higher during versus pre-COVID-19 for overall PDIs (6.39 vs 5.05; P < .001). In adjusted analyses, odds of postoperative sepsis were higher during COVID-19 versus pre-COVID-19 (adjusted odds ratio 1.28 [95% confidence interval 1.04–1.56]). The remainder of the PDIs did not have increased adjusted odds during compared with pre-COVID-19.

CONCLUSIONS:

Postoperative sepsis rates increased among children hospitalized during COVID-19. Efforts are needed to improve safety of postoperative care for hospitalized children.

The coronavirus disease 2019 (COVID-19) pandemic has dramatically impacted hospital systems throughout the United States, leading to policy and process changes to improve surge capacity and infection control strategies.1  Hospitals across the country have struggled to manage patient volume surges and clinical severity among adult patients with COVID-19, whereas pediatric health care systems experienced reductions in nonurgent health care (eg, elective surgeries, well-child visits) as well as decreased child emergency department visits and hospitalizations.2,3  With 2.5 million pediatric hospitalizations annually,4  the potential for decreased health care access and treatment delays associated with COVID-19 could result in deficiencies in the quality and safety of pediatric hospital care.1  To date, few data exist surrounding the impact of these pandemic-related changes on the safety of hospitalized children.

The Agency for Healthcare Research and Quality’s (AHRQ’s) Pediatric Quality Indicators (PDIs) are designed to leverage administrative data to assess pediatric hospital safety through identification of potentially preventable events related to hospital system exposure and guide provider- and system-level improvement strategies.57  Our study objectives were to assess changes in PDIs for hospitalized children during COVID-19 compared with pre-COVID-19. These findings can then inform pediatric hospital safety practices and policies during the current pandemic and in preparation for future pandemics.

We conducted a retrospective cohort study of pediatric discharges (inpatient or observation, children aged 0–18 years) in the Pediatric Health Information System (PHIS) database. Preliminary PHIS analyses revealed a significant decline in volume of children’s hospital encounters beginning on March 15, 2020. We therefore defined our study periods as March 15 to May 31, 2020 (during COVID-19) and March 15 to May 31, 2017–2019 (pre-COVID-19). The PHIS database contains administrative data, including 41 International Classification of Diseases 10th Revision diagnosis and procedure codes on discharges from 52 tertiary care children’s hospitals across the United States.8  The PHIS database represents ∼20% of pediatric hospitalizations to children’s hospitals nationally. Data quality is maintained jointly between the Children’s Hospital Association (Lenexa, KS) and participating hospitals. The 45 hospitals that provided data throughout the study period were included. Sample size was determined by these selection criteria.

The primary outcome was the rate of each PDI, calculated by using the AHRQ PDIs v2020 International Classification of Diseases 10th Revision, Clinical Modification and Procedure Coding System software (numerator and denominator definitions and predictor and confounder selection are detailed by the AHRQ).9  We calculated the overall and specific PDI rates within the pre-COVID-19 and COVID-19 periods, which were reported as PDI events per 1000 discharges. The overall PDI rate was calculated by pooling the individual measures’ numerators and denominators (ensuring that discharges were not double counted). We assessed patient characteristics (age, sex, and race and/or ethnicity), encounter type (medical or surgical, based on the assigned All Patient Refined Diagnosis Related Groups), payer, and disposition. We assessed for medical complexity using the number of complex chronic conditions (CCCs).10  We calculated the case-mix index for each hospitalization using the Hospitalization Resource Intensity Score for Kids (H-RISK), an All Patient Refined Diagnosis Related Groups–based measure of relative resource intensity specifically for pediatric hospital care and a proxy measure for clinical severity (a higher score indicates higher clinical severity).1113  Given rigorous PHIS data validation efforts, none of our included predictor variables had >1% missing, except race, which was missing in 4.2% of the patients. All patients missing race data were grouped into the “other” race category.

Categorical demographic and clinical characteristics were summarized by using frequencies and percentages and compared between periods by using χ2 tests for categorical variables and Student’s t tests for the H-RISK. Because the PDIs all have distinct population definitions, we tested them as 8 independent hypotheses rather than combined. Risk-adjusted PDI rates were compared between periods by using generalized linear mixed-effects models with a binary distribution and random intercepts for each hospital. For nonsurgical PDIs, models were adjusted for age, sex, race and/or ethnicity, encounter type, payer, CCC subtype, and H-RISK. For surgical PDIs, to account for differences related to surgical procedures, rather than adjusting for H-RISK, we replicated established methods for surgical safety risk adjustment from the National Surgical Quality Improvement Program by developing a linear risk score of an event (ie, the linear predictor from a logistic regression) for each principal procedure grouped using the AHRQ’s clinical classifications software (see Table 1 for example).1416  The linear risk scores were developed independently for each surgical PDI by using the Healthcare Cost and Utilization Project’s 2016 Kids’ Inpatient Database and applied to the PHIS at the discharge level. All statistical analyses were performed by using SAS version 9.4 (SAS Institute, Inc, Cary, NC). P values <.05 were considered statistically significant. This study was determined to be nonhuman subjects research by the institutional review board.

TABLE 1

Top Surgical Procedures With Excess Postoperative Sepsis Events During COVID-19

Surgical Procedures Based on CCSLinear Risk ScoreaNo. at Risk, nExpected No. Events, nNo. Experiencing Event, nExcess Events During COVID-19, n
Other OR heart procedures 1.6 664 16.6 21 
Organ transplant (other than bone marrow, corneal, or kidney) 9.6 50 8.2 11 
Other diagnostic cardiovascular procedures 6.0 28 1.3 
Incision of pleura, thoracentesis, chest drainage 1.3 25 0.6 
Heart valve procedures 1.4 242 5.1 
Surgical Procedures Based on CCSLinear Risk ScoreaNo. at Risk, nExpected No. Events, nNo. Experiencing Event, nExcess Events During COVID-19, n
Other OR heart procedures 1.6 664 16.6 21 
Organ transplant (other than bone marrow, corneal, or kidney) 9.6 50 8.2 11 
Other diagnostic cardiovascular procedures 6.0 28 1.3 
Incision of pleura, thoracentesis, chest drainage 1.3 25 0.6 
Heart valve procedures 1.4 242 5.1 

CCS, clinical classifications software; OR, operating room.

a

Linear risk score is the linear predictor of experiencing a postoperative sepsis event for the principal procedure CCS, which is calculated from the Healthcare Cost and Utilization Project’s 2016 Kids’ Inpatient Database and ranges from 0 to 14.4.

There were 399 113 pediatric hospital discharges in the pre-COVID-19 period (March to May 2017–2019) and 88 140 in the COVID-19 period (March 15 to May 31, 2020). During COVID-19, there was a lower discharge volume across all hospitals, with a median difference of −35.8% (interquartile range: −28.0% to −39.9%). During COVID-19, the majority of hospital discharges involved patients who were 0 to 4 years old (52% vs 49% pre-COVID-19), were female (51% vs 49% pre-COVID-19), and had government insurance (51% vs 53% pre-COVID-19). During COVID-19, the majority had a medical encounter (76% vs 77% pre-COVID-19), was discharged from the hospital (93% vs 93% pre-COVID-19), and had no CCCs (54% vs 55% pre-COVID-19). Compared with pre-COVID-19, hospital discharges during COVID-19 more often involved patients with surgical encounters, patients who were in the younger or older age categories, patients who were female, patients who had a nongovernment payer type, and patients with CCCs (Table 2; all P values significant at <.001). The mean H-RISKs were 2.96 (SE 5.9) and 3.47 (SE 6.9) in the pre-COVID-19 and COVID-19 periods, respectively.

TABLE 2

Characteristics of Pediatric Discharges, Pre-COVID-19 and COVID-19 Periods

Pre-COVID-19 (March 15 to May 30, 2017, 2018, 2019)During COVID-19 (March 15 to May 30, 2020)
Hospital discharges, N 399 113 88 140 
Encounter type, n (%)   
 Medical 308 635 (77.3) 66 777 (75.8) 
 Surgical 90 478 (22.7) 21 363 (24.2) 
Age, y, n (%)   
 0–4 196 204 (49.2) 45 389 (51.5) 
 5–9 56 147 (14.1) 10 179 (11.5) 
 10–14 62 913 (15.8) 13 285 (15.1) 
 15–18 51 128 (12.8) 11 755 (13.3) 
 18+ 32 721 (8.2) 7532 (8.5) 
Sex, n (%)   
 Male 202 133 (50.7) 43 641 (49.5) 
 Female 196 602 (49.3) 44 477 (50.5) 
Race and/or ethnicity, n (%)   
 Non-Hispanic white 184 068 (46.1) 40 865 (46.4) 
 Non-Hispanic Black 74 669 (18.7) 15 397 (17.5) 
 Hispanic 86 850 (21.8) 18 729 (21.2) 
 Asian American 14 319 (3.6) 3296 (3.7) 
 Othera 39 207 (9.8) 9853 (11.2) 
Payer type, n (%)   
 Government 212 283 (53.2) 44 873 (50.9) 
 Private 161 666 (40.5) 36 452 (41.4) 
 Other 25 164 (6.3) 6815 (7.7) 
Disposition, n (%)   
 Home health 12 868 (3.2) 2536 (2.9) 
 Home 370 539 (92.8) 81 632 (92.6) 
 Skilled facility 4032 (1) 944 (1.1) 
 Other 11 674 (2.9) 3028 (3.4) 
CCCs, n (%)   
 0 219 948 (55.1) 47 521 (53.9) 
 1–2 145 693 (36.5) 32 587 (37) 
 3+ 33 472 (8.4) 8032 (9.1) 
H-RISK, mean (SE) 2.96 (5.9) 3.47 (6.9) 
Pre-COVID-19 (March 15 to May 30, 2017, 2018, 2019)During COVID-19 (March 15 to May 30, 2020)
Hospital discharges, N 399 113 88 140 
Encounter type, n (%)   
 Medical 308 635 (77.3) 66 777 (75.8) 
 Surgical 90 478 (22.7) 21 363 (24.2) 
Age, y, n (%)   
 0–4 196 204 (49.2) 45 389 (51.5) 
 5–9 56 147 (14.1) 10 179 (11.5) 
 10–14 62 913 (15.8) 13 285 (15.1) 
 15–18 51 128 (12.8) 11 755 (13.3) 
 18+ 32 721 (8.2) 7532 (8.5) 
Sex, n (%)   
 Male 202 133 (50.7) 43 641 (49.5) 
 Female 196 602 (49.3) 44 477 (50.5) 
Race and/or ethnicity, n (%)   
 Non-Hispanic white 184 068 (46.1) 40 865 (46.4) 
 Non-Hispanic Black 74 669 (18.7) 15 397 (17.5) 
 Hispanic 86 850 (21.8) 18 729 (21.2) 
 Asian American 14 319 (3.6) 3296 (3.7) 
 Othera 39 207 (9.8) 9853 (11.2) 
Payer type, n (%)   
 Government 212 283 (53.2) 44 873 (50.9) 
 Private 161 666 (40.5) 36 452 (41.4) 
 Other 25 164 (6.3) 6815 (7.7) 
Disposition, n (%)   
 Home health 12 868 (3.2) 2536 (2.9) 
 Home 370 539 (92.8) 81 632 (92.6) 
 Skilled facility 4032 (1) 944 (1.1) 
 Other 11 674 (2.9) 3028 (3.4) 
CCCs, n (%)   
 0 219 948 (55.1) 47 521 (53.9) 
 1–2 145 693 (36.5) 32 587 (37) 
 3+ 33 472 (8.4) 8032 (9.1) 
H-RISK, mean (SE) 2.96 (5.9) 3.47 (6.9) 

Time periods were compared by using χ2 tests for categorical variables and Student’s t tests for the H-RISK. All comparisons are significant at P < .001.

a

“Other” includes those with other or missing race categories.

In unadjusted analyses, the overall PDI rate per 1000 discharges during COVID-19 was higher than the pre-COVID-19 PDI rate (6.39 vs 5.05 [P < .001]; Table 3). For specific PDIs, during COVID-19, there were higher rates of postoperative sepsis (13.12 vs 8.41; P < .001) and central venous catheter–related bloodstream infections (2.0 vs 1.58; P = .024) compared with the pre-COVID-19 period. After adjustment, there was no difference seen in the overall PDI rate, although there was significantly increased odds of postoperative sepsis (adjusted odds ratio 1.28; 95% confidence interval [CI] 1.04–1.56) during COVID-19 compared with the pre-COVID-19 period. We examined the most frequent procedures driving the increase in postoperative sepsis during COVID-19, and disproportionately higher rates were observed for “other heart procedures” (eg, insertion of implantable heart assist device; excess events = 4 per 1000); organ transplant (excess events = 3 per 1000); “other diagnostic cardiovascular” procedures (eg, insertion of implantable heart assist system into heart; excess events = 3 per 1000); incision of pleura, thoracentesis, and chest drainage (excess events = 3 per 1000); and heart valve procedures (excess events = 2 per 1000) (Table 1).

TABLE 3

Unadjusted and Adjusted Results for AHRQ PDIs

PDIUnadjusted Odds Ratio of Events per 1000 Discharges (95% CI)Adjusteda Odds Ratio of Events During COVID-19 Versus Pre-COVID-19 (95% CI)
Pre-COVID-19During COVID-19
Accidental puncture or laceration 0.85 (0.75–0.95) 0.99 (0.76–1.22) 0.95 (0.73–1.24) 
Iatrogenic pneumothorax 0.21 (0.16–0.26) 0.15 (0.06–0.25) 0.60 (0.31–1.18) 
Perioperative hemorrhage or hematomab 3.75 (3.07–4.44) 3.6 (2.06–5.14) 0.89 (0.56–1.42) 
Postoperative respiratory failureb 13.31 (11.76–14.86) 11.75 (8.34–15.16) 0.83 (0.6–1.15) 
Postoperative sepsisb 8.41 (7.62–9.2) 13.12 (10.99–15.25)* 1.28 (1.04–1.56)* 
Central venous catheter–related bloodstream infection 1.58 (1.43–1.73) 2.00 (1.63–2.36)* 0.96 (0.78–1.19) 
Neonatal bloodstream infection 21.50 (18.27–24.74) 24.13 (18.10–30.16) 1.02 (0.75–1.4) 
Overall 5.05 (4.81–5.28) 6.39 (5.82–6.97)* 0.97 (0.87–1.08) 
PDIUnadjusted Odds Ratio of Events per 1000 Discharges (95% CI)Adjusteda Odds Ratio of Events During COVID-19 Versus Pre-COVID-19 (95% CI)
Pre-COVID-19During COVID-19
Accidental puncture or laceration 0.85 (0.75–0.95) 0.99 (0.76–1.22) 0.95 (0.73–1.24) 
Iatrogenic pneumothorax 0.21 (0.16–0.26) 0.15 (0.06–0.25) 0.60 (0.31–1.18) 
Perioperative hemorrhage or hematomab 3.75 (3.07–4.44) 3.6 (2.06–5.14) 0.89 (0.56–1.42) 
Postoperative respiratory failureb 13.31 (11.76–14.86) 11.75 (8.34–15.16) 0.83 (0.6–1.15) 
Postoperative sepsisb 8.41 (7.62–9.2) 13.12 (10.99–15.25)* 1.28 (1.04–1.56)* 
Central venous catheter–related bloodstream infection 1.58 (1.43–1.73) 2.00 (1.63–2.36)* 0.96 (0.78–1.19) 
Neonatal bloodstream infection 21.50 (18.27–24.74) 24.13 (18.10–30.16) 1.02 (0.75–1.4) 
Overall 5.05 (4.81–5.28) 6.39 (5.82–6.97)* 0.97 (0.87–1.08) 

CCS, clinical classification software.

a

Models were adjusted for age, sex, race and/or ethnicity, encounter type, payer, CCC subtype, and H-RISK and controlled for hospital clustering.

b

The model included a linear risk score of the event for each principal procedure CCS derived from the Healthcare Cost and Utilization Project’s 2016 Kids’ Inpatient Database instead of H-RISK.

*

P < .05.

In this retrospective cohort study of children’s hospital discharges, we found an increased risk for overall safety events (ie, PDIs), postoperative sepsis, and central venous catheter bloodstream infections during the COVID-19 pandemic. However, after adjusting for patient and clinical factors, we found that the only PDI with increased odds during COVID-19 was postoperative sepsis. In light of the ongoing pandemic and morbidity and mortality associated with postoperative sepsis,17  efforts are needed to further understand this alarming finding to mitigate risk for children requiring surgery during the COVID-19 pandemic.

Because many elective procedures were canceled during the early COVID-19 pandemic, it is possible that patients undergoing surgeries were more critically ill compared with the pre-COVID-19 population. However, after risk adjusting for clinical severity, the increase for postoperative sepsis remained significant. Several factors may have contributed to this finding, including delays in presentation, diagnosis, and management. In multicenter risk adjustment studies, researchers have identified several risk factors for postoperative sepsis, including wound class (eg, dirty wounds), need for oxygen support, and higher American Society of Anesthesiologists classification.14  These factors are associated with more severe disease at time of operation, which could reflect delayed presentation as well as delayed diagnosis and definitive management. More severe presentations associated with delay in management has also been associated with intussusception, volvulus, bowel obstruction, and infections.1820  Studies in children with appendicitis reveal that lack of health insurance and decreased health care use can lead to delayed diagnosis and increased risk for complications, such as postoperative sepsis.2123 

To our knowledge, there is no literature on trends in hospital safety events during the COVID-19 pandemic, although there are anecdotal reports of increased hospital-acquired infections.24  After hospital presentation, there are many in-hospital factors impacted by the COVID-19 pandemic that could lead to increased risk for postoperative sepsis. These include delay in surgical management due to preoperative COVID-19 testing, modified workflow leading to delays in diagnostic testing, need for increased sterilization, and infection control precautions impacting operating room flow. It is also conceivable that changes in workflow and personnel and need for personal protective equipment for hospital staff could lead to delays in postoperative management. This could lead to delayed recognition and treatment of hospital-acquired infections that could then progress to sepsis. Although we were unable to ascertain the relative influence of pre- and in-hospital factors on the increase in postoperative sepsis, previous data have revealed that the COVID-19 pandemic has impacted health care access, including decreased health care use, thus leading to potential increased risk for postoperative sepsis.25 

Limitations of the study include the use of administrative data: the data set lacked certain clinical and other factors that may have impacted safety events in the pediatric hospital during COVID-19. We attempted to minimize this limitation using hierarchical modeling to adjust for PDI clustering among hospitals and used previously established methods for surgical PDI risk adjustment. Our risk adjustment methods were also based on administrative data and may not fully account for true clinical severity. Our findings are limited to freestanding children’s hospitals and thus may not be generalizable to other settings. Because our study was conducted in the early months of the COVID-19 pandemic, further study is needed to understand the full extent of changes in PDIs during the COVID-19 pandemic.

After adjusting for patient and clinical factors, we found increased odds for postoperative sepsis among children hospitalized during COVID-19. Further study is warranted to better understand the underlying drivers for this trend, especially regarding pre- and in-hospital factors that may influence the risk of sepsis after surgical procedures. This information can then inform efforts to improve timely presentation, diagnosis, and management in children requiring surgery to reduce morbidity and mortality in this population. Given the concern that the COVID-19 pandemic will persist for months to years, as well as concern for risk of future pandemics, continued efforts are warranted to ensure the safety of hospitalized children.

Dr Masonbrink conceptualized and designed the study, critically reviewed the study data, drafted the initial manuscript, and reviewed and revised the manuscript; Drs Kaiser, Hogan, Parikh, Clark, and Rangel participated in study design, critically reviewed the study data, and reviewed and revised the manuscript; Drs Hall and Harris conceptualized and designed the study, conducted data analyses, drafted sections of the initial manuscript, and critically reviewed and revised the manuscript; and all authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

FUNDING: Drs Parikh (K08HS024554) and Kaiser (K08HS024592) were supported by grants from the Agency for Healthcare Research and Quality. However, the content is solely the responsibility of the authors and does not necessarily represent the views of the Agency for Healthcare Research and Quality. The funding source had no role in the design and conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript. Funded by the National Institutes of Health (NIH).

1
AcademyHealth
.
Health systems respond to COVID-19: priorities for rapid-cycle evaluation
.
2020
.
2
Hartnett
KP
,
Kite-Powell
A
,
DeVies
J
, et al
;
National Syndromic Surveillance Program Community of Practice
.
Impact of the COVID-19 pandemic on emergency department visits - United States, January 1, 2019-May 30, 2020
.
MMWR Morb Mortal Wkly Rep
.
2020
;
69
(
23
):
699
704
3
Wilder
JL
,
Parsons
CR
,
Growdon
AS
,
Toomey
SL
,
Mansbach
JM
.
Pediatric hospitalizations during the COVID-19 pandemic
.
Pediatrics
.
2020
;
146
(
6
):
e2020005983
4
Moore
BJ
,
Freeman
WJ
,
Jiang
HJ
.
Costs of pediatric hospital stays, 2016
.
2019
.
5
Scanlon
MC
,
Harris
JM
 II
,
Levy
F
,
Sedman
A
.
Evaluation of the Agency for Healthcare Research and Quality pediatric quality indicators
.
Pediatrics
.
2008
;
121
(
6
).
6
Miller
MR
,
Elixhauser
A
,
Zhan
C
.
Patient safety events during pediatric hospitalizations
.
Pediatrics
.
2003
;
111
(
6 pt 1
):
1358
1366
7
Agency for Healthcare Research and Quality
.
Quality indicator user guide: pediatric quality indicators (PDI) composite measures. Version v2020
.
2020
.
8
Children’s Hospital Association
.
PHIS
.
Available at: https://www.childrenshospitals.org/phis. Accessed August 12, 2020
9
Agency for Healthcare Research and Quality
.
Pediatric Quality Indicators SAS QI Software [computer program]
.
Rockville, MD
:
Agency for Healthcare Research and Quality
;
2020
10
Feudtner
C
,
Feinstein
JA
,
Zhong
W
,
Hall
M
,
Dai
D
.
Pediatric complex chronic conditions classification system version 2: updated for ICD-10 and complex medical technology dependence and transplantation
.
BMC Pediatr
.
2014
;
14
:
199
11
Richardson
T
,
Rodean
J
,
Harris
M
,
Berry
J
,
Gay
JC
,
Hall
M
.
Development of Hospitalization Resource Intensity Scores for Kids (H-RISK) and comparison across pediatric populations
.
J Hosp Med
.
2018
;
13
(
9
):
602
608
12
Auger
KA
,
Harris
JM
,
Gay
JC
, et al
.
Progress (?) toward reducing pediatric readmissions
.
J Hosp Med
.
2019
;
14
(
10
):
618
621
13
Tchou
MJ
,
Hall
M
,
Shah
SS
, et al
;
Pediatric Research in Inpatient Settings (PRIS) Network
.
Patterns of electrolyte testing at children’s hospitals for common inpatient diagnoses
.
Pediatrics
.
2019
;
144
(
1
):
e20181644
14
American College of Surgeons National Surgical Quality Improvement Program Pediatric
.
ACS NSQIP Pediatric Semiannual Modeling Report February 12, 2020
.
Chicago, IL
:
American College of Surgeons
;
2020
15
Lam
S
,
Fridley
J
,
Desai
VR
, et al
.
Pediatric National Surgical Quality Improvement Program: useful for quality improvement in craniosynostosis surgery?
J Craniofac Surg
.
2016
;
27
(
3
):
605
611
16
Kuo
BJ
,
Vissoci
JRN
,
Egger
JR
, et al
.
Perioperative outcomes for pediatric neurosurgical procedures: analysis of the National Surgical Quality Improvement Program-Pediatrics
.
J Neurosurg Pediatr
.
2017
;
19
(
3
):
361
371
17
Miniño
AM
,
Xu
J
,
Kochanek
KD
.
Deaths: preliminary data for 2008
.
Natl Vital Stat Rep
.
2010
;
59
(
2
):
1
52
18
Cribbs
RK
,
Gow
KW
,
Wulkan
ML
.
Gastric volvulus in infants and children
.
Pediatrics
.
2008
;
122
(
3
).
19
Lampl
B
,
Levin
TL
,
Berdon
WE
,
Cowles
RA
.
Malrotation and midgut volvulus: a historical review and current controversies in diagnosis and management
.
Pediatr Radiol
.
2009
;
39
(
4
):
359
366
20
Millar
AJW
,
Rode
H
,
Cywes
S
.
Malrotation and volvulus in infancy and childhood
.
Semin Pediatr Surg
.
2003
;
12
(
4
):
229
236
21
Baxter
KJ
,
Nguyen
HTMH
,
Wulkan
ML
,
Raval
MV
.
Association of health care utilization with rates of perforated appendicitis in children 18 years or younger
.
JAMA Surg
.
2018
;
153
(
6
):
544
550
22
Bratton
SL
,
Haberkern
CM
,
Waldhausen
JH
.
Acute appendicitis risks of complications: age and Medicaid insurance
.
Pediatrics
.
2000
;
106
(
1 pt 1
):
75
78
23
Almaramhy
HH
.
Acute appendicitis in young children less than 5 years: review article
.
Ital J Pediatr
.
2017
;
43
(
1
):
15
24
Castellucci
M
.
Hospital-acquired infections may be rising as COVID-19 strains workforce
.
2020
.
25
Chaiyachati
BH
,
Agawu
A
,
Zorc
JJ
,
Balamuth
F
.
Trends in pediatric emergency department utilization after institution of coronavirus disease-19 mandatory social distancing
.
J Pediatr
.
2020
;
226
:
274
277.e1

Competing Interests

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.